Artificial Neural Network-Based Advanced Algorithm for New Generation Solar PV Systems

Solar PV has been one of the primary renewable energy sources in the current context due to its typical characteristics and sophisticated operation. Most of the time, the output of solar PV has been observed to be irregular and unpredictable; due to this, the load end gets stressed most of the time. Power generation via PhotoVoltaic (PV), due to their benefits such as ease of availability, low cost, negligible environmental pollution, lower maintenance tariff, has been gaining popularity compared to other available renewable resources. In order to minimize the drastic effects of changing environmental conditions over the output of the PV system, the Maximum Power Point Tracking (MPPT) technique has been adopted in most of the areas. It helps to keep track of the panel’s maximum power output to increase overall energy generation. Easy design, low cost, good performance characteristics with minimal output power variability, and the ability to monitor changing conditions easily and quickly are significant features of MPPT controllers. An MPPT system based on an improved neural network has been proposed and developed in the current study. As compared to existing software computing technologies and conventional powerpoint monitoring arrangements, the proposed system has a lower transient and steady-state response. Extensive research has been carried out on a standalone solar photovoltaic system for multidimensional performance analysis. The output has been studied, and considerable changes have been highlighted, followed by necessary explanations.